High-Level Architecture
Relevant source files
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WIREDUP is an integrated ecosystem consisting of a Python-based AI Auto-Wiring system and a high-performance Rust terminal (NexTerm). The project provides an intelligent infrastructure for AI agent orchestration, dependency injection, and standardized communication via the Model Context Protocol (MCP).
Sources: README.md:1-10, DEPLOYMENT_STATUS.md:15-45, public/index.html:158-180
System Overview
The architecture is divided into two primary domains: the Python AI Auto-Wiring System (Backend/Logic) and NexTerm (Frontend/Interface). These domains interact through an Auto-Wire Bridge that facilitates command routing, service discovery, and AI-driven suggestions.
The diagram above illustrates the bridge between the Rust-based terminal interface and the Python-based AI core.
Sources: public/index.html:158-180, rust-terminal/README.md:40-65, AGENT.md:25-45
Core Components
1. Python AI Auto-Wiring Engine
The core of the system is a dependency injection framework that automatically resolves and injects requirements for agents and services. It supports multiple scopes (e.g., Singleton) and handles circular detection.
| Component | Description | Key File |
|---|---|---|
| Auto-Wiring Engine | Intelligent DI for agents and services | src/core/autowire.py |
| Agent System | Extensible framework for research and execution agents | src/agents/base_agent.py |
| Skills Registry | Dynamic management of agent capabilities | src/agents/skills.py |
| MCP Protocol | Implementation of Model Context Protocol for AI messaging | src/mcp/protocol.py |
| SSH Manager | Secure remote connection and command execution | src/ssh/manager.py |
Sources: DEPLOYMENT_STATUS.md:20-33, README.md:55-75, AGENT.md:215-230
2. NexTerm (Rust Terminal)
NexTerm provides a modern Terminal User Interface (TUI) built with ratatui and crossterm. It features split panes, tab management, and real-time status panels reflecting the state of the Auto-Wiring system.
The NexTerm internal architecture manages user input and visual rendering while maintaining a connection to the AI backend.
Sources: rust-terminal/README.md:40-65, rust-terminal/src/ui/tui.rs:30-100, QUICKSTART.md:58-68
Data Flow and Interaction
Agent Execution Flow
When an agent is invoked, the system resolves dependencies through the AutoWire engine, sets up the AgentContext, and utilizes the SkillRegistry to perform tasks.
This flow demonstrates how the Auto-Wiring engine sits at the start of the lifecycle to prepare the agent for execution.
Sources: AGENT.md:65-95, README.md:55-75, SKILLS.md:260-280
Skill Management
Skills are discrete, reusable capabilities (e.g., Web Search, SSH Command) that are registered within a central SkillRegistry. Agents query this registry at runtime to perform specialized operations.
| Skill Type | Examples | Metadata |
|---|---|---|
| Research | Web Search, Scrape, Doc Analysis | Async, Cacheable |
| Execution | Code Execution, SSH Command | High Complexity |
| Reasoning | Text Gen, Classification, Logic | LLM-dependent |
| Communication | Email, Notification | External API-based |
Sources: SKILLS.md:44-245, AGENT.md:145-170
Infrastructure and Deployment
The project architecture supports multiple environments and secure remote operations.
- ENV Management: Features multi-source configuration with validation and sensitive data masking.
- SSH Security: Manages connection pooling and secure key rotation for remote agent deployment.
- Web Interface: A static frontend (deployed via Cloudflare Pages) provides architecture visualization and project status.
Sources: README.md:15-30, DEPLOYMENT_STATUS.md:50-75, public/index.html:110-140
Conclusion
The WIREDUP architecture leverages the performance of Rust for the user interface and the flexibility of Python for AI logic. By centralizing dependency management through the Auto-Wiring Engine and standardizing communication via MCP, the system creates a cohesive environment for complex AI agent orchestration.